from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print_time_report()
Daal4py_kmeans_short: 0h 0m 1s
Daal4py_ridge: 0h 0m 2s
Kmeans_short: 0h 0m 2s
Daal4py_logisticregression: 0h 0m 4s
Daal4py_kmeans_tall: 0h 0m 8s
Ridge: 0h 0m 10s
Logisticregression: 0h 0m 20s
Kmeans_tall: 0h 0m 22s
Daal4py_kneighborsclassifier_kd_tree: 0h 0m 33s
Daal4py_kneighborsclassifier: 0h 2m 40s
Kneighborsclassifier_kd_tree: 0h 2m 54s
Xgboost: 0h 5m 1s
Lightgbm: 0h 5m 4s
Catboost_symmetric: 0h 5m 7s
Histgradientboostingclassifier: 0h 5m 18s
Catboost_lossguide: 0h 5m 24s
Kneighborsclassifier: 0h 34m 19s
Total: 1h 7m 38s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.142 | 0.000 | 5.638 | 0.000 | 1 | 5 | NaN | NaN | 0.504 | 0.000 | 0.281 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 19.632 | 0.552 | 0.000 | 0.020 | 1 | 5 | 0.819 | 0.710 | 1.796 | 0.012 | 10.930 | 0.316 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.194 | 0.001 | 0.000 | 0.194 | 1 | 5 | 1.000 | 0.000 | 0.092 | 0.001 | 2.094 | 0.026 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.134 | 0.000 | 5.992 | 0.000 | 1 | 100 | NaN | NaN | 0.494 | 0.000 | 0.270 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 19.767 | 0.061 | 0.000 | 0.020 | 1 | 100 | 0.941 | 0.931 | 1.967 | 0.055 | 10.049 | 0.283 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.195 | 0.001 | 0.000 | 0.195 | 1 | 100 | 1.000 | 1.000 | 0.093 | 0.001 | 2.091 | 0.025 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.139 | 0.000 | 5.745 | 0.000 | -1 | 5 | NaN | NaN | 0.492 | 0.000 | 0.283 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 33.359 | 0.000 | 0.000 | 0.033 | -1 | 5 | 0.819 | 0.710 | 1.882 | 0.045 | 17.724 | 0.424 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.182 | 0.013 | 0.000 | 0.182 | -1 | 5 | 1.000 | 0.000 | 0.096 | 0.002 | 1.891 | 0.147 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.137 | 0.000 | 5.822 | 0.000 | -1 | 1 | NaN | NaN | 0.497 | 0.000 | 0.277 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 27.403 | 0.207 | 0.000 | 0.027 | -1 | 1 | 0.715 | 0.808 | 2.007 | 0.036 | 13.654 | 0.266 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.174 | 0.013 | 0.000 | 0.174 | -1 | 1 | 1.000 | 1.000 | 0.096 | 0.001 | 1.816 | 0.140 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.136 | 0.000 | 5.898 | 0.000 | 1 | 1 | NaN | NaN | 0.488 | 0.000 | 0.278 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 13.567 | 0.076 | 0.000 | 0.014 | 1 | 1 | 0.715 | 0.931 | 2.007 | 0.037 | 6.758 | 0.129 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.195 | 0.001 | 0.000 | 0.195 | 1 | 1 | 1.000 | 1.000 | 0.096 | 0.001 | 2.036 | 0.030 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.132 | 0.000 | 6.052 | 0.000 | -1 | 100 | NaN | NaN | 0.491 | 0.000 | 0.269 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 33.726 | 0.000 | 0.000 | 0.034 | -1 | 100 | 0.941 | 0.808 | 1.903 | 0.031 | 17.726 | 0.292 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.177 | 0.013 | 0.000 | 0.177 | -1 | 100 | 1.000 | 1.000 | 0.100 | 0.010 | 1.770 | 0.224 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.056 | 0.000 | 0.283 | 0.000 | 1 | 5 | NaN | NaN | 0.098 | 0.000 | 0.575 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.321 | 0.074 | 0.000 | 0.021 | 1 | 5 | 0.989 | 0.972 | 0.289 | 0.005 | 73.692 | 1.258 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.027 | 0.000 | 0.000 | 0.027 | 1 | 5 | 1.000 | 1.000 | 0.006 | 0.000 | 4.613 | 0.256 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.057 | 0.000 | 0.282 | 0.000 | 1 | 100 | NaN | NaN | 0.097 | 0.000 | 0.587 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.586 | 0.068 | 0.000 | 0.022 | 1 | 100 | 0.987 | 0.978 | 0.331 | 0.007 | 65.311 | 1.434 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.029 | 0.001 | 0.000 | 0.029 | 1 | 100 | 1.000 | 1.000 | 0.006 | 0.000 | 4.832 | 0.262 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.060 | 0.000 | 0.266 | 0.000 | -1 | 5 | NaN | NaN | 0.097 | 0.000 | 0.617 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 35.431 | 0.000 | 0.000 | 0.035 | -1 | 5 | 0.989 | 0.972 | 0.272 | 0.004 | 130.353 | 1.852 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.032 | 0.001 | 0.000 | 0.032 | -1 | 5 | 1.000 | 1.000 | 0.006 | 0.000 | 5.541 | 0.274 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.062 | 0.000 | 0.259 | 0.000 | -1 | 1 | NaN | NaN | 0.097 | 0.000 | 0.637 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 24.732 | 0.094 | 0.000 | 0.025 | -1 | 1 | 0.978 | 0.979 | 0.276 | 0.007 | 89.549 | 2.377 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.001 | 0.000 | 0.020 | -1 | 1 | 1.000 | 1.000 | 0.006 | 0.000 | 3.452 | 0.241 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.059 | 0.000 | 0.269 | 0.000 | 1 | 1 | NaN | NaN | 0.098 | 0.000 | 0.609 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 10.946 | 0.018 | 0.000 | 0.011 | 1 | 1 | 0.978 | 0.978 | 0.320 | 0.007 | 34.191 | 0.795 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.016 | 0.000 | 0.000 | 0.016 | 1 | 1 | 1.000 | 1.000 | 0.006 | 0.000 | 2.716 | 0.093 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.059 | 0.000 | 0.272 | 0.000 | -1 | 100 | NaN | NaN | 0.097 | 0.000 | 0.607 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 35.452 | 0.000 | 0.000 | 0.035 | -1 | 100 | 0.987 | 0.979 | 0.262 | 0.004 | 135.515 | 1.888 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.033 | 0.002 | 0.000 | 0.033 | -1 | 100 | 1.000 | 1.000 | 0.006 | 0.000 | 5.752 | 0.489 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.576 | 0.000 | 0.022 | 0.000 | -1 | 100 | NaN | NaN | 0.802 | 0.000 | 4.460 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 3.024 | 0.021 | 0.000 | 0.003 | -1 | 100 | 0.967 | 0.958 | 0.125 | 0.001 | 24.159 | 0.300 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.006 | 0.000 | 0.000 | 0.006 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 20.063 | 11.362 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.651 | 0.000 | 0.022 | 0.000 | -1 | 1 | NaN | NaN | 0.792 | 0.000 | 4.610 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.489 | 0.006 | 0.000 | 0.000 | -1 | 1 | 0.957 | 0.971 | 0.718 | 0.013 | 0.681 | 0.014 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 3.729 | 1.583 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.531 | 0.000 | 0.023 | 0.000 | 1 | 1 | NaN | NaN | 0.786 | 0.000 | 4.492 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.819 | 0.011 | 0.000 | 0.001 | 1 | 1 | 0.957 | 0.970 | 0.236 | 0.003 | 3.477 | 0.060 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 2.157 | 1.227 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.432 | 0.000 | 0.023 | 0.000 | 1 | 5 | NaN | NaN | 0.771 | 0.000 | 4.453 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.594 | 0.044 | 0.000 | 0.002 | 1 | 5 | 0.967 | 0.958 | 0.130 | 0.011 | 12.299 | 1.069 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 4.369 | 2.581 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.948 | 0.000 | 0.020 | 0.000 | 1 | 100 | NaN | NaN | 0.774 | 0.000 | 5.101 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 5.269 | 0.017 | 0.000 | 0.005 | 1 | 100 | 0.967 | 0.971 | 0.700 | 0.010 | 7.530 | 0.110 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.000 | 0.000 | 0.004 | 1 | 100 | 1.000 | 1.000 | 0.001 | 0.000 | 5.146 | 2.181 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.418 | 0.000 | 0.023 | 0.000 | -1 | 5 | NaN | NaN | 0.783 | 0.000 | 4.367 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.894 | 0.015 | 0.000 | 0.001 | -1 | 5 | 0.967 | 0.970 | 0.233 | 0.003 | 3.834 | 0.079 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 7.585 | 3.823 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.830 | 0.000 | 0.019 | 0.000 | -1 | 100 | NaN | NaN | 0.484 | 0.000 | 1.714 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.044 | 0.001 | 0.000 | 0.000 | -1 | 100 | 0.986 | 0.981 | 0.001 | 0.000 | 57.358 | 23.614 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 21.371 | 18.208 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.889 | 0.000 | 0.018 | 0.000 | -1 | 1 | NaN | NaN | 0.475 | 0.000 | 1.874 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.000 | 0.001 | 0.000 | -1 | 1 | 0.971 | 0.988 | 0.007 | 0.000 | 3.688 | 0.247 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 19.248 | 15.882 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.883 | 0.000 | 0.018 | 0.000 | 1 | 1 | NaN | NaN | 0.476 | 0.000 | 1.856 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.024 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.971 | 0.988 | 0.001 | 0.000 | 22.562 | 8.172 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.888 | 5.602 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.865 | 0.000 | 0.018 | 0.000 | 1 | 5 | NaN | NaN | 0.472 | 0.000 | 1.832 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.027 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.982 | 0.981 | 0.001 | 0.000 | 35.385 | 14.653 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 5.881 | 5.113 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.914 | 0.000 | 0.018 | 0.000 | 1 | 100 | NaN | NaN | 0.483 | 0.000 | 1.894 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.058 | 0.000 | 0.000 | 0.000 | 1 | 100 | 0.986 | 0.988 | 0.007 | 0.001 | 8.510 | 0.767 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.372 | 4.569 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.883 | 0.000 | 0.018 | 0.000 | -1 | 5 | NaN | NaN | 0.474 | 0.000 | 1.864 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.982 | 0.988 | 0.001 | 0.000 | 25.404 | 8.378 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 23.831 | 21.778 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.589 | 0.0 | 0.815 | 0.000 | random | NaN | 30 | NaN | 0.433 | 0.0 | 1.360 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.399 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 7.557 | 4.723 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.141 | 7.918 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.574 | 0.0 | 0.837 | 0.000 | k-means++ | NaN | 30 | NaN | 0.378 | 0.0 | 1.517 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.403 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 7.604 | 4.685 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.885 | 8.459 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.643 | 0.0 | 3.613 | 0.000 | random | NaN | 30 | NaN | 3.179 | 0.0 | 2.090 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.230 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.586 | 2.608 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.020 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.041 | 5.945 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.587 | 0.0 | 3.644 | 0.000 | k-means++ | NaN | 30 | NaN | 2.997 | 0.0 | 2.198 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.300 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.617 | 2.447 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.020 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.489 | 6.999 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.234 | 0.0 | 0.014 | 0.000 | k-means++ | NaN | 20 | NaN | 0.087 | 0.0 | 2.671 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.185 | 0.000 | k-means++ | -0.000 | 20 | 0.001 | 0.001 | 0.0 | 2.603 | 0.618 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 10.163 | 7.875 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.077 | 0.0 | 0.041 | 0.000 | random | NaN | 20 | NaN | 0.033 | 0.0 | 2.353 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.189 | 0.000 | random | 0.001 | 20 | 0.000 | 0.001 | 0.0 | 2.471 | 0.605 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.800 | 5.888 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.602 | 0.0 | 0.266 | 0.000 | k-means++ | NaN | 20 | NaN | 0.355 | 0.0 | 1.697 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.681 | 0.000 | k-means++ | 0.306 | 20 | 0.338 | 0.001 | 0.0 | 1.974 | 0.335 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 6.532 | 3.309 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.225 | 0.0 | 0.713 | 0.000 | random | NaN | 20 | NaN | 0.153 | 0.0 | 1.463 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.899 | 0.000 | random | 0.342 | 20 | 0.326 | 0.001 | 0.0 | 1.873 | 0.368 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.013 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 6.617 | 3.790 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 11.460 | 0.0 | [-0.1029581] | 0.000 | NaN | NaN | NaN | NaN | NaN | 2.111 | 0.0 | 5.430 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [54.53203843] | 0.000 | NaN | NaN | NaN | NaN | 0.544 | 0.000 | 0.0 | 0.819 | 0.424 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.20330007] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.423 | 0.376 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [28] | 0.885 | 0.0 | [-2.32204547] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.843 | 0.0 | 1.050 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [28] | 0.002 | 0.0 | [133.85272173] | 0.000 | NaN | NaN | NaN | NaN | 0.210 | 0.003 | 0.0 | 0.591 | 0.142 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [28] | 0.000 | 0.0 | [20.51244484] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.001 | 0.0 | 0.164 | 0.111 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.190 | 0.0 | 0.421 | 0.0 | NaN | NaN | NaN | 0.202 | 0.0 | 0.941 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.012 | 0.0 | 6.499 | 0.0 | NaN | NaN | 0.111 | 0.021 | 0.0 | 0.587 | 0.008 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.0 | 0.649 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 1.023 | 1.914 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.473 | 0.0 | 0.543 | 0.0 | NaN | NaN | NaN | 0.250 | 0.0 | 5.887 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.0 | 4.646 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.725 | 0.567 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.0 | 0.013 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.627 | 0.664 | See | See |